# Computations
import pandas as pd
import numpy as np
import calendar

# sklearn
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler

# Visualisation libraries

## Progress Bar
import progressbar

## Text
from colorama import Fore, Back, Style
from IPython.display import Image, display, Markdown, Latex, clear_output

## plotly
from plotly.offline import init_notebook_mode, iplot 
import plotly.graph_objs as go
import plotly.offline as py
from plotly.subplots import make_subplots
import plotly.express as px

## seaborn
import seaborn as sns
sns.set_style("whitegrid")
sns.set_context("paper", rc={"font.size":12,"axes.titlesize":14,"axes.labelsize":12})

## matplotlib
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
from matplotlib.patches import Ellipse, Polygon
import matplotlib.gridspec as gridspec
import matplotlib.colors
from pylab import rcParams
plt.style.use('seaborn-whitegrid')
import matplotlib as mpl
mpl.rcParams['figure.figsize'] = (17, 6)
mpl.rcParams['axes.labelsize'] = 14
mpl.rcParams['xtick.labelsize'] = 12
mpl.rcParams['ytick.labelsize'] = 12
mpl.rcParams['text.color'] = 'k'
%matplotlib inline

import warnings
warnings.filterwarnings("ignore")

Home / Business Analytics and Modeling

Bank Marketing Dataset

• Preprocessing
• Exploratory Data Analysis
• Modeling: PyTorch Multi-layer Perceptron (MLP) for Binary classification
• Modeling: Keras Multi-layer Perceptron (MLP) for Binary classification

Bank Marketing Dataset

In this article, we work on a dataset available from the UCI Machine Learning Repository. The data is related to direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe to a term deposit (variable y).

This dataset is based on "Bank Marketing" UCI dataset (please check the description at archive.ics.uci.edu/ml/datasets/Bank+Marketing). The data is enriched by the addition of five new social and economic features/attributes (national wide indicators from a ~10M population country), published by the Banco de Portugal and publicly available at: bportugal.pt/estatisticasweb. This dataset is almost identical to the one used in [Moro et al., 2014] (it does not include all attributes due to privacy concerns).

Dataset Information:

The data is related to the direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.

There are four datasets:

1. bank-additional-full.csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014]
2. bank-additional.csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs.
3. bank-full.csv with all examples and 17 inputs, ordered by date (older version of this dataset with fewer inputs).
4. bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this dataset with fewer inputs).

The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).

Loading the Dataset

The zip file includes two datasets:

1. bank-additional-full.csv with all examples, ordered by date (from May 2008 to November 2010).
2. bank-additional.csv with 10% of the examples (4119), randomly selected from bank-additional-full.csv. The smallest dataset is provided to test more computationally demanding machine learning algorithms (e.g., SVM).

The binary classification goal is to predict if the client will subscribe a bank term deposit (variable y).

Path = 'Data/bank-additional-full.csv'
Data = pd.read_csv(Path, sep=';')
Target = 'Term Deposit Subscription'
Labels = ['No', 'Yes']
display(Data.head(10).style.hide_index().set_precision(2))

age	job	marital	education	default	housing	loan	contact	month	day_of_week	duration	campaign	pdays	previous	poutcome	emp.var.rate	cons.price.idx	cons.conf.idx	euribor3m	nr.employed	y
56	housemaid	married	basic.4y	no	no	no	telephone	may	mon	261	1	999	0	nonexistent	1.10	93.99	-36.40	4.86	5191.00	no
57	services	married	high.school	unknown	no	no	telephone	may	mon	149	1	999	0	nonexistent	1.10	93.99	-36.40	4.86	5191.00	no
37	services	married	high.school	no	yes	no	telephone	may	mon	226	1	999	0	nonexistent	1.10	93.99	-36.40	4.86	5191.00	no
40	admin.	married	basic.6y	no	no	no	telephone	may	mon	151	1	999	0	nonexistent	1.10	93.99	-36.40	4.86	5191.00	no
56	services	married	high.school	no	no	yes	telephone	may	mon	307	1	999	0	nonexistent	1.10	93.99	-36.40	4.86	5191.00	no
45	services	married	basic.9y	unknown	no	no	telephone	may	mon	198	1	999	0	nonexistent	1.10	93.99	-36.40	4.86	5191.00	no
59	admin.	married	professional.course	no	no	no	telephone	may	mon	139	1	999	0	nonexistent	1.10	93.99	-36.40	4.86	5191.00	no
41	blue-collar	married	unknown	unknown	no	no	telephone	may	mon	217	1	999	0	nonexistent	1.10	93.99	-36.40	4.86	5191.00	no
24	technician	single	professional.course	no	yes	no	telephone	may	mon	380	1	999	0	nonexistent	1.10	93.99	-36.40	4.86	5191.00	no
25	services	single	high.school	no	yes	no	telephone	may	mon	50	1	999	0	nonexistent	1.10	93.99	-36.40	4.86	5191.00	no

Number of Instances	Number of Attributes
41188	21

Bank Client Data

Feature	Description
Age	numeric
Job	Type of Job (categorical: "admin.","blue-collar","entrepreneur","housemaid","management","retired","self-employed","services","student","technician","unemployed","unknown")
Marital	marital status (categorical: "divorced","married","single","unknown"; note: "divorced" means divorced or widowed)
Education	(categorical: "basic.4y","basic.6y","basic.9y","high.school","illiterate","professional.course","university.degree","unknown")
Default	has credit in default? (categorical: "no","yes","unknown")
Housing	has housing loan? (categorical: "no","yes","unknown")
Loan	has personal loan? (categorical: "no","yes","unknown")

Education_Dict = {'basic.4y':1,'basic.6y':2,'basic.9y':3,'high.school':4,
                  'illiterate':0,'professional.course':5,'university.degree':6,'unknown':-1}
Dataset_Subcategories = {}
Dataset_Subcategories['Bank Client Data'] = [x.title() for x in Data.iloc[:,:7].columns]

Related with the Last Contact of the Current Campaign

Feature	Description
Contact	contact communication type (categorical: "cellular","telephone")
Month	last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec")
Day of week	last contact day of the week (categorical: "mon","tue","wed","thu","fri")
Duration	last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y="no"). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

Dataset_Subcategories['Related with the Last Contact of the Current Campaign'] = \
                                                [x.title().replace('_',' ') for x in Data.iloc[:,7:11].columns]

Other Attributes

Feature	Description
Campaign	number of contacts performed during this campaign and for this client (numeric, includes last contact)
Pdays	number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)
Previous	number of contacts performed before this campaign and for this client (numeric)
Poutcome	outcome of the previous marketing campaign (categorical: "failure","nonexistent","success")

Dataset_Subcategories['Other Attributes'] = [x.title() for x in Data.iloc[:,11:15].columns]

Social and Economic Context Attributes

Feature	Description
emp.var.rate	employment variation rate - quarterly indicator (numeric)
cons.price.idx	consumer price index - monthly indicator (numeric)
cons.conf.idx	consumer confidence index - monthly indicator (numeric)
euribor3m	euribor* 3 month rate - daily indicator (numeric)
nr.employed	number of employees - quarterly indicator (numeric)

* the basic rate of interest used in lending between banks on the European Union interbank market and also used as a reference for setting the interest rate on other loans.

Dataset_Subcategories['Social and Economic Context Attributes'] = ['Employment Variation Rate','Consumer Price Index',
                                               'Consumer Confidence Index','Euribor three Month Rate', 'Number of Employees']

Output variable (Desired Target):

Feature	Description
y	has the client subscribed a term deposit? (binary: "yes","no")

Dataset_Subcategories['Output variable (Desired Target)'] = ['Term Deposit Subscription']

Columns

Columns = []
for x in Dataset_Subcategories.keys():
    Columns.extend(Dataset_Subcategories[x])
    
Data.columns = Columns

Data.head()

	Age	Job	Marital	Education	Default	Housing	Loan	Contact	Month	Day Of Week	...	Campaign	Pdays	Previous	Poutcome	Employment Variation Rate	Consumer Price Index	Consumer Confidence Index	Euribor three Month Rate	Number of Employees	Term Deposit Subscription
0	56	housemaid	married	basic.4y	no	no	no	telephone	may	mon	...	1	999	0	nonexistent	1.1	93.994	-36.4	4.857	5191.0	no
1	57	services	married	high.school	unknown	no	no	telephone	may	mon	...	1	999	0	nonexistent	1.1	93.994	-36.4	4.857	5191.0	no
2	37	services	married	high.school	no	yes	no	telephone	may	mon	...	1	999	0	nonexistent	1.1	93.994	-36.4	4.857	5191.0	no
3	40	admin.	married	basic.6y	no	no	no	telephone	may	mon	...	1	999	0	nonexistent	1.1	93.994	-36.4	4.857	5191.0	no
4	56	services	married	high.school	no	no	yes	telephone	may	mon	...	1	999	0	nonexistent	1.1	93.994	-36.4	4.857	5191.0	no

5 rows × 21 columns

Preprocessing

def Data_Plot(Inp):
    data_info = Inp.dtypes.astype(str).to_frame(name='Data Type')
    Temp = Inp.isnull().sum().to_frame(name = 'Number of NaN Values')
    data_info = data_info.join(Temp, how='outer')
    data_info ['Size'] = Inp.shape[0]
    data_info['Percentage'] = 100 - np.round(100*(data_info['Number of NaN Values']/Inp.shape[0]),2)
    data_info = data_info.reset_index(drop = False).rename(columns = {'index':'Features'})
    #
    fig = px.bar(data_info, x= 'Features', y= 'Percentage', color = 'Data Type', text = 'Data Type',
                color_discrete_sequence = ['PaleGreen', 'LightCyan', 'PeachPuff', 'Pink', 'Plum'],
                 hover_data = data_info.columns)
    fig.update_layout(plot_bgcolor= 'white', legend=dict(x=1.01, y=.5, traceorder="normal",
                                                         bordercolor="DarkGray", borderwidth=1))
    fig.update_traces(texttemplate= 6*' ' + '%{label}', textposition='inside')
    fig.update_traces(marker_line_color= 'Black', marker_line_width=1., opacity=1)
    fig.show()
    return data_info
    
def dtypes_group(Inp, Dict = False):
    Temp = Inp.dtypes.to_frame(name='Data Type').sort_values(by=['Data Type'])
    Out = pd.DataFrame(index =Temp['Data Type'].unique(), columns = ['Features','Count'])
    for c in Temp['Data Type'].unique():
        Out.loc[Out.index == c, 'Features'] = [Temp.loc[Temp['Data Type'] == c].index.tolist()]
        Out.loc[Out.index == c, 'Count'] = len(Temp.loc[Temp['Data Type'] == c].index.tolist())
    Out.index.name = 'Data Type'
    Out = Out.reset_index(drop = False)
    Out['Data Type'] = Out['Data Type'].astype(str)
    
    if Dict:
        Out = dict(zip(Out['Data Type'], Out['Features']))
    return Out

_ = Data_Plot(Data)
dType = dtypes_group(Data, Dict = True)

Chaing column categorical vairabvle to title format (for EDA):.

Categorical_Variables = dType['object']
# For EDA
for Feat in Categorical_Variables:
    Data[Feat] = Data[Feat].apply(lambda x: x.title())
del Feat

# Eduation
Data['Education'] = Data['Education'].apply(lambda x: x.title().replace('.',' '))
Education_Dict = dict(zip([x.title().replace('.',' ') for x in Education_Dict.keys()],Education_Dict.values()))
display(Data.head(10).style.hide_index().set_precision(2))

Data['Day Of Week'] = Data['Day Of Week'].replace(dict(zip([x for x in calendar.day_abbr],[x for x in calendar.day_name] )))

# Saving to a CSV file
Data.to_csv (Path.split(".")[0]+'_EDA.csv', index = None, header=True)

Age	Job	Marital	Education	Default	Housing	Loan	Contact	Month	Day Of Week	Duration	Campaign	Pdays	Previous	Poutcome	Employment Variation Rate	Consumer Price Index	Consumer Confidence Index	Euribor three Month Rate	Number of Employees	Term Deposit Subscription
56	Housemaid	Married	Basic 4Y	No	No	No	Telephone	May	Mon	261	1	999	0	Nonexistent	1.10	93.99	-36.40	4.86	5191.00	No
57	Services	Married	High School	Unknown	No	No	Telephone	May	Mon	149	1	999	0	Nonexistent	1.10	93.99	-36.40	4.86	5191.00	No
37	Services	Married	High School	No	Yes	No	Telephone	May	Mon	226	1	999	0	Nonexistent	1.10	93.99	-36.40	4.86	5191.00	No
40	Admin.	Married	Basic 6Y	No	No	No	Telephone	May	Mon	151	1	999	0	Nonexistent	1.10	93.99	-36.40	4.86	5191.00	No
56	Services	Married	High School	No	No	Yes	Telephone	May	Mon	307	1	999	0	Nonexistent	1.10	93.99	-36.40	4.86	5191.00	No
45	Services	Married	Basic 9Y	Unknown	No	No	Telephone	May	Mon	198	1	999	0	Nonexistent	1.10	93.99	-36.40	4.86	5191.00	No
59	Admin.	Married	Professional Course	No	No	No	Telephone	May	Mon	139	1	999	0	Nonexistent	1.10	93.99	-36.40	4.86	5191.00	No
41	Blue-Collar	Married	Unknown	Unknown	No	No	Telephone	May	Mon	217	1	999	0	Nonexistent	1.10	93.99	-36.40	4.86	5191.00	No
24	Technician	Single	Professional Course	No	Yes	No	Telephone	May	Mon	380	1	999	0	Nonexistent	1.10	93.99	-36.40	4.86	5191.00	No
25	Services	Single	High School	No	Yes	No	Telephone	May	Mon	50	1	999	0	Nonexistent	1.10	93.99	-36.40	4.86	5191.00	No

Categorical Variables

Yes/No Features

First, let's convert all Yes/No columns using as follows

$$\begin{cases} -1 & \mbox{Unknown}\\0 &\mbox{No}\\ 1 &\mbox{Yes}\end{cases}$$

def Header(Text, L = 100, C = 'Blue', T = 'White'):
    BACK = {'Black': Back.BLACK, 'Red':Back.RED, 'Green':Back.GREEN, 'Yellow': Back.YELLOW, 'Blue': Back.BLUE,
         'Magenta':Back.MAGENTA, 'Cyan': Back.CYAN}
    FORE = {'Black': Fore.BLACK, 'Red':Fore.RED, 'Green':Fore.GREEN, 'Yellow':Fore.YELLOW, 'Blue':Fore.BLUE,
         'Magenta':Fore.MAGENTA, 'Cyan':Fore.CYAN, 'White': Fore.WHITE}
    print(BACK[C] + FORE[T] + Style.NORMAL + Text + Style.RESET_ALL + ' ' + FORE[C] +
          Style.NORMAL +  (L- len(Text) - 1)*'=' + Style.RESET_ALL)
    
def Line(L=100, C = 'Blue'):
    FORE = {'Black': Fore.BLACK, 'Red':Fore.RED, 'Green':Fore.GREEN, 'Yellow':Fore.YELLOW, 'Blue':Fore.BLUE,
         'Magenta':Fore.MAGENTA, 'Cyan':Fore.CYAN, 'White': Fore.WHITE}
    print(FORE[C] + Style.NORMAL + L*'=' + Style.RESET_ALL)
    
def List_Print(Text, List, C = 'Blue', T = 'White'):
    BACK = {'Black': Back.BLACK, 'Red':Back.RED, 'Green':Back.GREEN, 'Yellow': Back.YELLOW, 'Blue': Back.BLUE,
            'Magenta':Back.MAGENTA, 'Cyan': Back.CYAN}
    FORE = {'Black': Fore.BLACK, 'Red':Fore.RED, 'Green':Fore.GREEN, 'Yellow':Fore.YELLOW, 'Blue':Fore.BLUE,
            'Magenta':Fore.MAGENTA, 'Cyan':Fore.CYAN, 'White': Fore.WHITE}
    print(BACK[C] + FORE[T] + Style.NORMAL + '%s:' % Text + Style.RESET_ALL + ' %s' % ', '.join(List))

df = Data.copy()
YN_Feat = []
for c in Categorical_Variables:
    s = set(df[c].unique().tolist())
    if s.issubset({'No', 'Yes', 'Unknown'}):
        YN_Feat.append(c)
del c, s
List_Print('Yes/No Features', YN_Feat)

# Converting:
Temp = {'Yes':1, 'No':0, 'Unknown':-1}
for c in YN_Feat:
    df[c] = df[c].replace(Temp).astype(int)    
del c
display(df[YN_Feat].head().style.hide_index())

## Adding these keys and values to a dictionary
CatVar_dict = {}
for c in YN_Feat:
    CatVar_dict[c] = Temp
    
#substracting YN Features from Categorical_Variables
Categorical_Variables = list(set(Categorical_Variables) - set(YN_Feat))
del YN_Feat, Temp, c

Yes/No Features: Housing, Loan, Default, Term Deposit Subscription

Housing	Loan	Default	Term Deposit Subscription
0	0	0	0
0	0	-1	0
1	0	0	0
0	0	0	0
0	1	0	0

Moreover,

List_Print('Remaining categorical features', Categorical_Variables, C = 'Red')

Remaining categorical features: Marital, Education, Job, Month, Poutcome, Day Of Week, Contact

Poutcome

For these features, we have,

$$\mbox{Poutcome} = \begin{cases} -1 & \mbox{Nonexistent}\\0 &\mbox{Failure}\\ 1 &\mbox{Success}\end{cases}$$

Temp = {'Success':1, 'Failure':0, 'Nonexistent':-1}
df['Poutcome'] = df['Poutcome'].replace(Temp).astype(int)  
CatVar_dict['Poutcome'] = Temp
del Temp

Marital

$$\mbox{Poutcome} = \begin{cases} -1 & \mbox{Unknown}\\0 &\mbox{Single}\\ 1 &\mbox{Married}\\ 2 &\mbox{Divorced}\end{cases}$$

Temp = {'Divorced':2, 'Married':1, 'Single':0, 'Unknown':-1}
df['Marital'] = df['Marital'].replace(Temp).astype(int)  
CatVar_dict['Marital'] = Temp
del Temp

Day Of Week

$$\mbox{Day Of Week} = \begin{cases} 0 & \mbox{Monday}\\ 1 &\mbox{Tuesday}\\ 2 &\mbox{Wednesday}\\ 3 &\mbox{Thursday}\\ 4 &\mbox{Friday}\\ 5 &\mbox{Saturday}\\ 6 &\mbox{Sunday} \end{cases}$$

df['Day Of Week'] = df['Day Of Week'].replace(dict(zip([x for x in calendar.day_name], np.arange(7)))).astype(int)  

Contact

$$\mbox{Contact} = \begin{cases} 0 & \mbox{Telephone}\\ 1 &\mbox{Cellular} \end{cases}$$

df['Contact'] = df['Contact'].replace(dict(zip(df['Contact'].unique(), np.arange(len(df['Contact'].unique()))))).astype(int)  

# Job
Temp = {'Unknown':-1, 'Unemployed':0, 'Student': 1, 'Housemaid':2, 
        'Retired':3, 'Blue-Collar':4, 'Self-Employed': 5, 'Services':6,
        'Technician':7, 'Admin.':8, 'Management':9, 'Entrepreneur':10 }
df['Job'] = df['Job'].replace(Temp).astype(int)  
del Temp

# Month
df['Month'] = df['Month'].replace(dict(zip([x for x in calendar.month_abbr][1:], np.arange(12)))).astype(int)  

# Education
df['Education'] = df['Education'].replace(Education_Dict).astype(int)  

# Pdays
df.loc[df['Pdays'] == 999, 'Pdays'] = -1

def LogRegPlot(Feat, df = df, Target = Target, C ='HotPink', EC = 'DeepPink' ):
    X = df[Feat].values.reshape(-1,1)
    Test = np.arange(df[Feat].min(), df[Feat].max()).reshape(-1,1)
    y = df[Target].values.reshape(-1,1)
    logr = LogisticRegression(solver='newton-cg')
    _ = logr.fit(X, y)
    Pred_Prop = logr.predict_proba(Test)

    fig, ax = plt.subplots(1, 1, figsize=(16,5))

    # Right plot
    _ = ax.scatter(X, y, color=C, edgecolor = EC)
    _ = ax.plot(Test, Pred_Prop[:,1], color='MidnightBlue', lw = 1)
    Temp = ax.get_xlim()
    _ = ax.hlines(0, Temp[0], Temp[1], linestyles='dashed', lw=1)
    _ = ax.hlines(1, Temp[0], Temp[1], linestyles='dashed', lw=1)
    _ = ax.set_xlim(Temp)
    _ = ax.set_xlabel('Last Contact Duration (in seconds)')
    _ = ax.set_ylabel('Probability of %s' % Target)
    _ = ax.set_title('Estimated Probability of %s using Logistic Regression' % Target, weight='medium', fontsize = 14 )
    
LogRegPlot('Duration')
LogRegPlot('Pdays', C='Lime', EC = 'LimeGreen')

Numerical Variables

def FeatBins(Inp, Bins):
    Out = pd.cut(Inp, bins = pd.IntervalIndex.from_tuples([(x, y) for x, y in zip(Bins[:-1],Bins[1:])]))
    Temp = np.sort(Out.astype('str').unique())
    Dict = dict(zip(Temp, np.arange(len(Temp))))
    Out = Out.astype('str').replace(Dict)
    return Out

# Age
df['Age'] = FeatBins(df['Age'], [14, 24, 40, 59, 80, 100])

# Campaign
df['Campaign'] = FeatBins(df['Campaign'], [0, 5, 10, 20, 50, 100])

# Consumer Confidence Index
df['Consumer Confidence Index'] = FeatBins(df['Consumer Confidence Index'], [-60, -40, -30, -20])
# Consumer Price Index
df['Consumer Price Index'] = FeatBins(df['Consumer Price Index'], [92, 93, 94, 95])

# Duration
df['Duration'] = FeatBins(df['Duration'], [-1, 100, 200, 400, 800, 1000, 5000])

# Employment Variation Rate
df['Employment Variation Rate'] = FeatBins(df['Employment Variation Rate'], [-4, -3, -2, -1, 0, 1, 2])

# Euribor three Month Rate
df['Euribor three Month Rate'] = FeatBins(df['Euribor three Month Rate'], [0, 1, 2, 3, 4, 5, 6])

# Number of Employees
df['Number of Employees'] = FeatBins(df['Number of Employees'], [4960, 5000, 5030, 5100, 5200, 5300])

# Pdays
## Negative one here means unknown
df['Pdays'] = FeatBins(df['Pdays'], [-2, 0, 5, 10, 20, 30])

Variance of the Features

Let's take a look at the variance of the features.

Fig, ax = plt.subplots(figsize=(17,16))
Temp = df.drop(columns = [Target]).var().sort_values(ascending = False).to_frame(name= 'Variance').round(2).T

_ = sns.heatmap(Temp, ax=ax, annot=True, square=True,  cmap =sns.color_palette("OrRd", 20),
                  linewidths = 0.8, vmin=0, vmax=Temp.max(axis =1)[0],
                  cbar_kws={'label': 'Feature Variance', "aspect":40, "shrink": .4, "orientation": "horizontal"})
labels = [x.replace(' ','\n').replace('Euribor\nthree','Euribor three').replace('\nof\n',' of\n')
          for x in [item.get_text() for item in ax.get_xticklabels()]]
_ = ax.set_xticklabels(labels)
_ = ax.set_yticklabels('')

Furthermore, we would like to standardize features by removing the mean and scaling to unit variance. In this article, we demonstrated the benefits of scaling data using StandardScaler().

# Columns
Temp = df.drop(columns = Target).columns.tolist()
# Scaling
scaler = StandardScaler()
_ = scaler.fit(df[Temp])
df[Temp] = scaler.transform(df[Temp])

# Variance Plot
Fig, ax = plt.subplots(figsize=(17,16))

Temp = df.drop(columns = [Target]).var().sort_values(ascending = False).to_frame(name= 'Variance').round(2).T

_ = sns.heatmap(Temp, ax=ax, annot=True, square=True,  cmap =sns.color_palette('Greens'),
                  linewidths = 0.8, vmin=0, vmax=Temp.max(axis =1)[0],
                  cbar_kws={'label': 'Feature Variance', "aspect":40, "shrink": .4, "orientation": "horizontal"})
labels = [x.replace(' ','\n').replace('Euribor\nthree','Euribor three').replace('\nof\n',' of\n')
          for x in [item.get_text() for item in ax.get_xticklabels()]]
_ = ax.set_xticklabels(labels)
_ = ax.set_yticklabels('')

Feature Correlation

Fig, ax = plt.subplots(figsize=(17,16))
Temp = df.corr().round(2)
Temp = Temp.loc[(Temp.index == Target)].drop(columns = Target).T.sort_values(by = Target).T
_ = sns.heatmap(Temp, ax=ax, annot=True, square=True,  cmap =sns.color_palette("Greens", n_colors=10),
                linewidths = 0.8, vmin=0, vmax=1,
                annot_kws={"size": 12},
                cbar_kws={'label': Target + ' Correlation', "aspect":40, "shrink": .4, "orientation": "horizontal"})
labels = [x.replace(' ','\n').replace('Euribor\nthree','Euribor three').replace('\nof\n',' of\n')
          for x in [item.get_text() for item in ax.get_xticklabels()]]
_ = ax.set_xticklabels(labels)
_ = ax.set_yticklabels('')

Saving to a CSV

df.to_csv (Path.split(".")[0]+'_STD.csv', index = None, header=True)

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Refrences

1. S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

2. S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimaraes, Portugal, October, 2011. EUROSIS. [bank.zip]

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